Project Summary As we enter into an era of personalized (precision) medicine, it becomes increasingly important to define the factors that confer disease risk and outcome. Since these determinants cannot be easily controlled in human epidemiological studies, genetically-engineered mouse (GEM) strains and human induced pluripotent stem cell (iPSC) lines provide mechanistically-tractable platforms to define the factors underlying disease heterogeneity and translate them to risk assessment tools and treatments. This challenge is nicely illustrated by Neurofibromatosis type 1 (NF1), a rare neurogenetic condition caused by a germline mutation in the NF1 gene. Individuals with NF1 are prone to the development of a wide variety of neurological problems, including cognitive and behavioral problems (60-70% of children) and low-grade brain tumors (~20% of children). While establishing the diagnosis of NF1 in an infant is straightforward, it is currently not possible to predict which child will develop future medical problems, determine whether there will be clinical progression requiring treatment, or institute effective therapies that specifically target the subtype of clinical manifestation in that individual. The pressing challenge for clinicians and researchers alike is to dissect the genetic, cellular, molecular, and systems-level etiologies for these common neurologic problems with the ultimate goal of developing prognostic and precision neurology approaches for children affected with NF1. In this proposal, we plan to leverage human NF1-patient iPSCs, mice engineered with patient germline NF1 gene mutations, bioinformatics and systems biology approaches, and novel modeling approaches to mechanistically define the factors that underlie the heterogeneity that characterizes NF1. The overall mission of this project is to establish the etiologic bases for NF1 clinical variability and to create a blueprint for future clinical application necessary to transform the care of individuals with NF1 from a reactive to a more proactive approach.